Selection and mutation on microRNA target sequences during rice evolution

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) posttranscriptionally down-regulate gene expression by binding target mRNAs. Analysis of the evolution of miRNA binding sites is helpful in understanding the co-evolution between miRNAs and their targets. To understand this process in plants a comparative analysis of miRNA-targeted duplicated gene pairs derived from a well-documented whole genome duplication (WGD) event in combination with a population genetics study of six experimentally validated miRNA binding sites in rice (<it>O. sativa</it>) was carried out.</p> <p>Results</p> <p>Of the 1,331 pairs of duplicate genes from the WGD, 41 genes (29 pairs) were computationally predicted to be miRNA targets. Sequence substitution analysis indicated that the synonymous substitution rate was significantly lower in the miRNA binding sites than their 5' and 3' flanking regions. Of the 29 duplicated gene pairs, 17 have only one paralog been targeted by a miRNA. This could be due to either gain of a miRNA binding site after the WGD or because one of the duplicated genes has escaped from being a miRNA target after the WGD (loss of miRNA binding site). These possibilities were distinguished by separating miRNAs conserved in both dicots and monocot plants from rice-specific miRNAs and by phylogenetic analysis of miRNA target gene families. The gain/loss rate of miRNA binding sites was estimated to be 3.0 × 10^-9gain/loss per year. Most (70.6%) of the gains/losses were due to nucleotide mutation. By analysis of cultivated (<it>O. sativa</it>; <it>n </it>= 30) and wild (<it>O. rufipogon</it>; <it>n </it>= 15) rice populations, no segregating site was observed in six miRNA binding sites whereas 0.12–0.20 SNPs per 21-nt or 1.53–1.80 × 10^-3of the average pairwise nucleotide diversity (π) were found in their flanking regions.</p> <p>Conclusion</p> <p>Both molecular evolution and population genetics support the hypothesis that conservation of miRNA binding sites is maintained by purifying selection through elimination of deleterious alleles. Nucleotide mutations play a major role in the gain/loss of miRNA binding sites during evolution.</p

REMAS: a new regression model to identify alternative splicing events from exon array data

<p>Abstract</p> <p>Background</p> <p>Alternative splicing (AS) is an important regulatory mechanism for gene expression and protein diversity in eukaryotes. Previous studies have demonstrated that it can be causative for, or specific to splicing-related diseases. Understanding the regulation of AS will be helpful for diagnostic efforts and drug discoveries on those splicing-related diseases. As a novel exon-centric microarray platform, exon array enables a comprehensive analysis of AS by investigating the expression of known and predicted exons. Identifying of AS events from exon array has raised much attention, however, new and powerful algorithms for exon array data analysis are still absent till now.</p> <p>Results</p> <p>Here, we considered identifying of AS events in the framework of variable selection and developed a regression method for AS detection (REMAS). Firstly, features of alternatively spliced exons were scaled by reasonably defined variables. Secondly, we designed a hierarchical model which can represent gene structure and transcriptional influence to exons, and the lasso type penalties were introduced in calculation because of huge variable size. Thirdly, an iterative two-step algorithm was developed to select alternatively spliced genes and exons. To avoid negative effects introduced by small sample size, we ranked genes as parameters indicating their AS capabilities in an iterative manner. After that, both simulation and real data evaluation showed that REMAS could efficiently identify potential AS events, some of which had been validated by RT-PCR or supported by literature evidence.</p> <p>Conclusion</p> <p>As a new lasso regression algorithm based on hierarchical model, REMAS has been demonstrated as a reliable and effective method to identify AS events from exon array data.</p

UDTIRI: An Open-Source Road Pothole Detection Benchmark Suite

It is seen that there is enormous potential to leverage powerful deep learning methods in the emerging field of urban digital twins. It is particularly in the area of intelligent road inspection where there is currently limited research and data available. To facilitate progress in this field, we have developed a well-labeled road pothole dataset named Urban Digital Twins Intelligent Road Inspection (UDTIRI) dataset. We hope this dataset will enable the use of powerful deep learning methods in urban road inspection, providing algorithms with a more comprehensive understanding of the scene and maximizing their potential. Our dataset comprises 1000 images of potholes, captured in various scenarios with different lighting and humidity conditions. Our intention is to employ this dataset for object detection, semantic segmentation, and instance segmentation tasks. Our team has devoted significant effort to conducting a detailed statistical analysis, and benchmarking a selection of representative algorithms from recent years. We also provide a multi-task platform for researchers to fully exploit the performance of various algorithms with the support of UDTIRI dataset.Comment: Database webpage: https://www.udtiri.com/, Kaggle webpage: https://www.kaggle.com/datasets/jiahangli617/udtir

The optimal design and operation strategy of renewable energy-CCHP coupled system applied in five building objects

Abstract(#br)Combined cooling, heating, and power (CCHP) is an economic and eco-friendly technology to mitigate energy issues with remarkable energy efficiency improvement. This study formulates a mixed integer nonlinear programming (MINLP) model for a combined CCHP system coupled with renewable energy, i.e. RCCHP system, which is applied in five different buildings to evaluate the economic and environmental performance under two optimization modes. Net present value (NPV), internal rate of return (IRR) and dynamic payback period (DPP) are introduced as economic indexes, while CO 2 emission reduction rate (CER) is considered as the environmental indicator to determine the optimal combination, capacity, and operation strategies for energy technologies. Results indicate that a combination of electricity purchased at valley period during night with power generated by the combined heating and power (CHP) unit coupled with wind turbine in peak period during daytime is cost-optimal which also enables higher energy efficiency. Meanwhile, the feed-in tariff as well as the uncoordinated electrical and thermal loads both show a significant impact on real-time operation strategies. Compared with the reference separate production (SP) system, the combined system shows better performance when applied to shopping mall under both optimization modes, e.g., with NPV up to 67.65 and 46.61 million RMB, IRR up to 20.70% and 25.10%, and the minimum DPP is 5.49 and 4.82 years under NPV and IRR maximization, respectively

Inspiratory-Activated Airway Vagal Preganglionic Neurones Excited by Thyrotropin-Releasing Hormone via Multiple Mechanisms in Neonatal Rats

The airway vagal preganglionic neurons (AVPNs) providing projections to intrinsic tracheobronchial ganglia are considered to be crucial to modulation of airway resistance in physiological and pathological states. AVPNs classified into inspiratory-activated AVPNs (IA-AVPNs) and inspiratory-inhibited AVPNs (II-AVPNs) are regulated by thyrotropin-releasing hormone (TRH)-containing terminals. TRH causes a direct excitatory current and attenuates the phasic inspiratory glycinergic inputs in II-AVPNs, however, whether and how TRH influences IA-AVPNs remains unknown. In current study, TRH regulation of IA-AVPNs and its mechanisms involved were investigated. Using retrogradely fluorescent labeling method and electrophysiology techniques to identify IA-AVPNs in brainstem slices with rhythmic inspiratory hypoglossal bursts recorded by a suction electrode, the modulation of TRH was observed with patch-clamp technique. The findings demonstrate that under voltage clamp configuration, TRH (100 nM) caused a slow excitatory inward current, augmented the excitatory synaptic inputs, progressively suppressed the inhibitory synaptic inputs and elicited a distinctive electrical oscillatory pattern (OP). Such a current and an OP was independent of presynaptic inputs. Carbenoxolone (100 μM), a widely used gap junction inhibitor, fully suppressed the OP with persistence of TRH-induced excitatory slow inward current and augment of the excitatory synaptic inputs. Both tetrodotoxin (1 μM) and riluzole (20 μM) functioned to block the majority of the slow excitatory inward current and prevent the OP, respectively. Under current clamp recording, TRH caused a slowly developing depolarization and continuously progressive oscillatory firing pattern sensitive to TTX. TRH increased the firing frequency in response to injection of a square-wave current. The results suggest that TRH excited IA-AVPNs via the following multiple mechanisms: (1) TRH enhances the excitatory and depresses the inhibitory inputs; (2) TRH induces an excitatory postsynaptic slow inward current; (3) TRH evokes a distinctive OP mediated by gap junction

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

Abstract: It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk.

It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes

Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index

Recent genetic association studies have identified 55 genetic loci associated with obesity or body mass index (BMI). The vast majority, 51 loci, however, were identified in European-ancestry populations. We conducted a meta-analysis of associations between BMI and ∼2.5 million genotyped or imputed single nucleotide polymorphisms among 86 757 individuals of Asian ancestry, followed by in silico and de novo replication among 7488–47 352 additional Asian-ancestry individuals. We identified four novel BMI-associated loci near the KCNQ1 (rs2237892, P = 9.29 × 10−13), ALDH2/MYL2 (rs671, P = 3.40 × 10−11; rs12229654, P = 4.56 × 10−9), ITIH4 (rs2535633, P = 1.77 × 10−10) and NT5C2 (rs11191580, P = 3.83 × 10−8) genes. The association of BMI with rs2237892, rs671 and rs12229654 was significantly stronger among men than among women. Of the 51 BMI-associated loci initially identified in European-ancestry populations, we confirmed eight loci at the genome-wide significance level (P < 5.0 × 10−8) and an additional 14 at P < 1.0 × 10−3 with the same direction of effect as reported previously. Findings from this analysis expand our knowledge of the genetic basis of obesity